et aloccup Environ Med KarhulaK, etal. Occup Environ Med 2018;75: doi: /oemed

Transcription

1

2 et aloccup Environ Med KarhulaK, etal. Occup Environ Med 2018;75: doi: /oemed

3 This study is part of the Finnish Public Sector (FPS) study comprising employees of 10 towns and six hospital districts in Finland. In this study, the inclusion criteria were survey data from 2008, 2012 and 2015 FPS surveys of the six hospital districts (response rates 72%, 71% and 69%, respectively), current contract of employment with period-based work (114:45 hours/3 week) and using Titania shift scheduling software.23 As described earlier,13 23 the individual surveys were linked to the payroll data of working hours from the 91 days prior to answering each of the questionnaires. Additionally, the employees were required to have a period-based work contract (114:45 hours/3 weeks) and to have at least 31 work shifts during the 91-day period (figure 1). Regular day workers were excluded as they do not work on a period-based work contract. Additionally, fixed evening and night shift workers were also excluded. The sample included 2481 employees of which 93% (n=2 303) were women and 7% men (n=178). Employees average age was 44 years (SD 8.15), and they had on average 15 years shift work experience (SD 9.83). Majority of participants had a full-time work (87%, n=2 155) and children under 18 years living in the same household (90%, n=1 278). The employees most common job titles were nurse (40%, n=966), departmental secretary (10%, n=238), practical nurse (6%, n=138), laboratory nurse (4%, n=107) and X-ray nurse (4%, n=103). The payroll-based daily working hour data was retrieved from the shift scheduling programme (Titania, CGI Finland, Helsinki, Finland). Validated sampling software (provided by CGI Finland) was used to retrieve the data from realised rosters (see Härmä et al27 for further details). The payroll data that were used in this study have been described in detail earlier.27 The working hour characteristics were those previously used.13 We utilised dichotomised variables of percentage of long work weeks (cut-point >25% for 40 hour/ week and cut-point >25% for 48 hour/week), percentage of evening and night shifts of all the work shifts (cut-point >10%), percentage of quick returns (<11 hour shift interval) of all shift intervals (cut-point >25%), percentage of weekend work of all the weekends (cut point >25%) and percentage of single free days of all the free days (cut-point >25%). Each shift-dependent KarhulaK, etal. Occup Environ Med 2018;75: doi: /oemed Occup Environ Med: first published as /oemed on 24 January Downloaded from on 18 June 2018 by guest. Protected by copyright. series of 2-year follow-ups showed that longer working hours distinguished those with a chronic work-family conflict from those whose WLC diminished. Additionally, employment in a high-status occupation has been associated with chronic workfamily conflict in both genders.21 In a 4-year follow-up among physicians, on-call duties were associated with higher levels of work interference with family.22 To date, the majority of the studies have lacked objective data of working hours and have been based on self-reports to assess exposure to different aspects of working hours. Survey data can bias the assessment of shift work especially among shift workers without night shifts.23 Furthermore, the earlier studies of shift work and WLC have had other methodological problems, for example, homogenous populations, lack of or a short follow-up time, low overall or follow-up response rate and little adjustment for confounders To tackle these earlier methodological challenges, this study had the unique opportunity to use a large cohort data with objective payroll data on working times and accounted for multiple confounders. Unsocial working hour characteristics refer to long or non-standard working hour characteristics, that is, working at socially valuable times of the day and week and having time off during socially less valuable times.26 In a previous cross-sectional study,13 we found an increased likelihood of WLC in association with several unsocial working hour characteristics. With this follow-up data, we aimed to investigate whether changes in unsocial working hour characteristics (long weekly working hours, evening and night work, quick returns, single days off and weekend work) would result into parallel changes in WLC.

4

5

6